The exemplary embodiment relates to opinion mining, and finds particular application in the context of the development of a natural language based opinion mining system.
Opinion mining refers to the determination of the attitude of a speaker or a writer with respect to some topic, written in natural language, using techniques involving natural language processing, computational linguistics, and text mining. Opinion mining is of particular interest to businesses seeking to obtain the opinions of customers and other reviewers on their products and services. Opinions are often expressed on social networks, blogs, e-forums, and in dedicated customer feedback pages of company websites.
Opinions are often expressed in natural language text using specific words, which can be considered as having a sentiment which is positive, i.e., indicating that the author has a good opinion of the item that he is writing about, or negative, i.e., that the author has a bad opinion. The words used can include adjectives (beautiful, ugly), verbs (love, hate), nouns (talent, nuisance), and sometimes adverbs (admirably, annoyingly). Each of these pairs includes a positive and a negative example. One problem in building an opinion mining system is the acquisition of a polar vocabulary, i.e., the vocabulary of positive and negative examples used to express positive and negative opinions. Words can have different meanings in different domains, thus a polar vocabulary can be ambiguous and differ from one applicative domain to another. For example, in the domain of movie reviews, the adjective “flat” is rather negative (“a flat scene”) while it is generally neutral, or even positive, for example, in blogs related to diet (“a flat stomach”).
It would be advantageous to be able to generate a polar vocabulary which is applicable to opinion mining in a particular domain.